• Journal of Convergence Society for SMB
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• v.6 no.4
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• pp.79-84
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• 2016

A novel diagnosis scheme PCADP(personalized computer aided diagnosis probability) is proposed to overcome the problems mentioned above. PCADP scheme is a personalized diagnosis method based on ontology and it makes the bio-data analysis just a 'process' in the Smart healthcare service system. In addition, we offer a semantics modeling of the smart healthcare ontology framework in order to describe smart healthcare data and service specifications as meaningful representations based on this PCADP. The PCADP scheme is a kind of statistical diagnosis method which has real-time processing, characteristics of flexible structure, easy monitoring of decision process, and continuous improvement.

• The KIPS Transactions:PartB
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• v.12B no.6 s.102
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• pp.655-660
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• 2005

The number of broadcasting channels and contents are increasing with the arrival of digital broadcast and various broadcasting medium. However, there is a limit on searching of the program by using conventional program guide. Therefore, personalized broadcasting service to provide the environment, that user is able to consume his/her customized broadcasting contents at anytime, is standardized by TV-Anytime Forum. Especially. the structure of data broadcasting contents have different from that of AV contents. Therefore, it is necessary to new structure of the metadata for personalized data broadcasting service and new mechanism for data flow of broadcasting system. In this paper, we introduce a method for personalized data broadcasting service using TV-Anytime metadata.

• The Journal of the Korea Contents Association
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• v.15 no.9
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• pp.1-10
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• 2015

We propose and implement a personalized tour recommendation system based on ontology. We utilize user's profile, dynamic information on search in the application, web search, and facebook for personalized recommendation. We construct tour database for England based on ontology for a demo service, and recommend tour spot considering an individual preference with tour database. This dynamic and personalized tour service makes it possible for individual to plan one's own tour by considering recommended tour spots for each individual.

• Journal of Broadcast Engineering
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• v.9 no.4 s.25
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• pp.345-356
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• 2004

In this paper, we present a design and implementation of a personalized broadcasting system using TV-Anytime metadata for providing personalized services. The TV-Anytime specifies metadata schema, metadata coding and delivery, and provides service models to provide personalized broadcasting content services at anytime when users want to consume using metadata, which includes ECG (Electronic Content Guide) and content descriptive information in a PDR (Personal Digital Recorder)-centric environment. The proposed personalized broadcasting system consists of a server that provides metadata binary-coding, encapsulation and multiplexing, and a client terminal that takes charge of de-multiplexing, metadata decoding, and metadata processing for personalized content accessing and consumption. This paper presents the details of the design of each functional module, and the evaluation results with a set of service scenarios in an end-to-end broadcasting test-bed.

• Information Systems Review
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• v.23 no.3
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• pp.51-75
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• 2021

Global e-commerce websites offer personalized recommendation services to gain sustainable competitiveness. Existing studies have offered personalized recommendation services using quantitative preferences such as ratings. However, offering personalized recommendation services using only quantitative data has raised the problem of decreasing recommendation performance. For example, a user gave a five-star rating but wrote a review that the user was unsatisfied with hotel service and cleanliness. In such cases, has problems where quantitative and qualitative preferences are inconsistent. Recently, a growing number of studies have considered review data simultaneously to improve the limitations of existing personalized recommendation service studies. Therefore, in this study, we identify review and rating mismatches and build a new user profile to offer personalized recommendation services. To this end, we use deep learning algorithms such as CNN, LSTM, CNN + LSTM, which have been widely used in sentiment analysis studies. And extract sentiment features from reviews and compare with quantitative preferences. To evaluate the performance of the proposed methodology in this study, we collect user preference information using real-world hotel data from the world's largest travel platform TripAdvisor. Experiments show that the proposed methodology in this study outperforms the existing other methodologies, using only existing quantitative preferences.

• The Journal of the Korea Contents Association
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• v.16 no.10
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• pp.435-442
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• 2016

Obesity extends the global economic burden and it causes that the failure of a reduction of physical activity, and diet management. In this work, nutritional information and personalized diet based on calorie supply system and is discriminatory utilized the obesity-related SNP information in order to recommend a personalized functional foods. This study performed a GWAS analysis for the excavation of a Korean-specific and obesity-related SNP, which utilizes genetic information were recommended by entering a personalized diet in accordance with the SNP genotype-phenotype information. In addition, we integrated Database with relation of nutrient for utilizing the USDA Food information and it was applied to recommend with Sickdanome. As a result, the obesity-related SNP information was confirmed in the sample which has the normal value BMI. In this study, we have recognized that the phenotype information related obesity, BMI is inconsistent with the SNP genotype information. This result is shown that it is necessary to provide the personalized dietary SickdanOme recommendation based on the both pheotype-genotype information.

• Journal of the Korean Institute of Intelligent Systems
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• v.17 no.6
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• pp.773-779
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• 2007

Internet bulletin boards have been used to exchange their idea and information among Internet users. But the existing Internet bulletin boards can not satisfy user's personal view. In this raper, Web 2.0 based customized Internet bulletin board is to design. The proposed Internet bulletin board provides each user with personalized information which are established by user beforehand. So user can retrieve his interested information fast. Moreover user can generate his own personalized bulletin board to collect one's interested information automatically. The personalized bulletin board is connected to several Internet bulletin boards with RSS feeds.

• Journal of the Korea Society of Computer and Information
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• v.22 no.12
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• pp.117-123
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• 2017

The personalized search algorithm is a search system that analyzes the user's IP, cookies, log data, and search history to recommend the desired information. As a result, users are isolated in the information frame recommended by the algorithm. This is called 'Filter bubble' phenomenon. Most of the personalized data can be deleted or changed by the user, but data stored in the service provider's server is difficult to access. This study suggests a way to neutralize personalization by keeping on sending random query words. This is to confuse the data accumulated in the server while performing search activities with words that are not related to the user. We have analyzed the rank change of the URL while conducting the search activity with 500 random query words once using the personalized account as the experimental group. To prove the effect, we set up a new account and set it as a control. We then searched the same set of queries with these two accounts, stored the URL data, and scored the rank variation. The URLs ranked on the upper page are weighted more than the lower-ranked URLs. At the beginning of the experiment, the difference between the scores of the two accounts was insignificant. As experiments continue, the number of random query words accumulated in the server increases and results show meaningful difference.

• Journal of Korean Library and Information Science Society
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• v.43 no.3
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• pp.353-384
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• 2012

This study was conducted on the observation that the filter bubble and privacy violation problems are related to the personalized services provided by libraries. This study discussed whether there is the possibility for invasion of privacy when libraries provide services utilizing state-of-the-art technology, such as location-based services, context aware services, RFID-based services, Cloud Services, and book recommendation services. In addition, this study discussed the following three aspects: whether or not users give up their right to privacy when they provide personal information for online services, whether or not there are discussions about users' privacy in domestic libraries, and what kind of risks the filter bubble problem can cause library users and what are possible solutions. This study represents early-stage research on library privacy in Korea, and can be used as basic data for privacy research.

• Proceedings of the KAIS Fall Conference
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• 2003.11a
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• pp.118-126
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• 2003

In data mining, having access to large amount of data sets for the purpose of predictive data does not guarantee good method, even where the size of Real data is Mobile commerce unlimited. In addition to searching expected Goods objects for Users, it becomes necessary to develop a recommendation service based on XML. In this paper, we design the optimized XML Recommender product data. Efficient XML data preprocessing is required, include of formatting, structural, and attribute representation with dependent on User Profile Information. Our goal is to find a relationship among user interested products from E-Commerce and M-Commerce to XDB. Firstly, analyzing user profiles information. In the result creating clusters with analyzed user profile such as with set of sex, age, job. Secondly, it is clustering XML data which are associative products classify from user profile in shopping mall. Thirdly, after composing categories and goods data in which associative objects exist from the first clustering, it represent categories and goods in shopping mall and optimized clustering XML data which are personalized products. The proposed personalized user profile clustering method has been designed and simulated to demonstrate it's efficient.